library(dplyr)

## Set working directory
## to Dataverse replication folder

survey <- read.table("Harris_Data/Harris 1989 Public Opinion Survey, study no. 891105/harris_s891105_spss.tab", header = TRUE)

### fix everything below for this survey

# pid
survey$pid <- c(1:nrow(survey))

# study
survey$study <-"891105"

# study year (year)
survey$year <- 1989

# geographic data (urban)
survey$urban <- NA

# geographic data (region)
survey$region <- NA

# respondent head of household (hh)
survey$hh <- NA

# increasing inequality (inequality)
summary(survey$Q6_B)
survey$inequality <- dplyr::recode(as.character(survey$Q6_B),
                            `1` = "Feel",
                            `2` = "Don't Feel",
                            `3` = "Not Sure")
table(survey$inequality)
table(survey$Q6_B)

# inequality variable (inequality.variable)
survey$inequality.variable <- 1

# union (union.self)
table(survey$F4_1)
survey$union.self <- dplyr::recode(survey$F4_1,
                            `0` = "No",
                            `1` = "Yes")
table(survey$union.self)

survey$union.other <- dplyr::recode(survey$F4_2,
                             `0` = "No",
                             `1` = "Yes")
table(survey$union.other)
table(survey$F4_4) # not sure
survey[survey$F4_4 == 1, c("union.self", "union.other", "F4_3")]

survey$union.self[survey$F4_4 == 1] <- "Not Sure"
survey$union.other[survey$F4_4 == 1] <- "Not Sure"


table(survey$union.self)
table(survey$union.other)

# employment (employed)
survey$employed <- NA

# emply self
survey$employed.self <- NA
# occupation COMBINED
survey$occupation <- dplyr::recode(survey$F1,
                            `1` = "Professional",
                            `2` = "Manager, official",
                            `3` = "Proprietor (small business)",
                            `4` = "Clerical worker",
                            `5` = "Sales worker",
                            `6` = "Skilled craftsman, foreman",
                            `7` = "Operative, unskilled laborer (except farm)",
                            `8` = "Service worker",
                            `9` = "Farmer, farm manager, farm laborer",
                            `10` = "Student",
                            `11` = "Housewife",
                            `12` = "Military service",
                            `13` = "Unemployed",
                            `14` = "Retired",
                            `15` = "Welfare",
                            `16` = "Disabled",
                            `17` = "Other (specify)",
                            `18` = "Not sure")
table(survey$occupation)

## occ self
survey$occupation.self <- NA

# household size (hhsize)
survey$hhsize <- NA

# education (educ)
table(survey$F3)
survey$educ <- dplyr::recode(survey$F3,
                      `1` = "Less than high school",
                      `2` = "High school graduate",
                      `3` = "Some college",
                      `4` = "College graduate",
                      `5` = "Post graduate",
                      `6` = "Not sure")
table(survey$educ)

# household income (income)
survey$income <- dplyr::recode(survey$F10,
                        `1` = "7,500 or less",
                        `2` = "7,501 to 15,000",
                        `3` = "15,001 to 25,000",
                        `4` = "25,001 to 35,000",
                        `5` = "35,001 to 50,000",
                        `6` = "50,001 to 75,000",
                        `7` = "75,001 to 100,000",
                        `8` = "100,001 and over",
                        `9` = "Not sure")
table(survey$income)

# age
survey$age <- dplyr::recode(survey$F2,
                     `1` = "18 to 20",
                     `2` = "21 to 24",
                     `3` = "25 to 29",
                     `4` = "30 to 34",
                     `5` = "35 to 39",
                     `6` = "40 to 44",
                     `7` = "45 to 49",
                     `8` = "50 to 64",
                     `9` = "65 to 74",
                     `10` = "75 and over",
                     `11` = "Not sure") # in codebook as x, but values of F2 variable suggest otherwise
table(survey$age)

# race
survey$race1 <- dplyr::recode(survey$F13,
                      `1` = "White",
                      `2` = "Black",
                      `3` = "Asian or Pacific Islander",
                      `4` = "American Indian or Alaskan native",
                      `5` = "Not sure")
survey$race2 <- dplyr::recode(survey$F12,
                              `3` = "Decline/not sure",
                              `1` = "Yes, hispanic",
                              `2` = "No, not hispanic")
table(survey$race1)
table(survey$race2)
sum(is.na(survey$race1))

survey$race <- ifelse(survey$race1 == "Not sure" |
                        is.na(survey$race1), 
                      "Decline/not sure", ifelse(survey$race1 == "White",
                                                 ifelse(survey$race2 == "Decline/not sure" |
                                                          is.na(survey$race2),
                                                        "Decline/not sure",
                                                        ifelse(survey$race2 == "No, not hispanic",
                                                               "Non-Hispanic White",
                                                               "Hispanic White")), "Non-white"))
table(survey$race)
table(survey$race[survey$race1 == "White"])

# politics (party)
survey$party <- dplyr::recode(survey$F7,
                       `1` = "Republican",
                       `2` = "Democrat",
                       `3` = "Independent",
                       `4` = "Other (vol.)",
                       `5` = "Not sure")
table(survey$party)

# politics (ideology)
survey$ideology <- dplyr::recode(survey$F6,
                          `1` = "Conservative",
                          `2` = "Moderate",
                          `3` = "Liberal",
                          `4` = "Not sure")
table(survey$ideology)

# gender, "original data"
summary(survey$S1)
survey$gender <- (dplyr::recode(survey$S1,
                                      `1` = "Male",
                                      `2` = "Female"))
table(survey$gender)

# religion
survey$religion <- dplyr::recode(survey$F5A,
                          `1` = "Protestant",
                          `2` = "Catholic",
                          `3` = "Jewish",
                          `4` = "Other (specify)",
                          `5` = "None (vol.)",
                          `6` = "Not sure")
table(survey$religion)

#factuals
survey$factual1 <- NA
survey$factual2 <- NA
survey$factual3 <- NA

## alienation index
survey$dontcare <- dplyr::recode(survey$Q6_A,
                                 `1` = "Feel",
                                 `2` = "Don t feel",
                                 `3` = "Not sure")
survey$dontcount <- dplyr::recode(survey$Q6_C,
                                  `1` = "Feel",
                                  `2` = "Don t feel",
                                  `3` = "Not sure")
survey$leftout <- dplyr::recode(survey$Q6_D,
                                `1` = "Feel",
                                `2` = "Don t feel",
                                `3` = "Not sure")

### question placement
survey$question_place <- "before party"

# subset
survey_891105 <- survey[,c("pid", "study", "year", "urban", "region", "hh",
                           "inequality", "inequality.variable", "union.self", "union.other",
                           "employed", "employed.self", "occupation", "occupation.self", "hhsize", "educ", "income", 
                           "age", "race", "party", "ideology", "gender", "religion",
                           "factual1", "factual2", "factual3", "dontcare", "dontcount", "leftout",
                           "question_place")]

# save file
#saveRDS(survey_891105, file = "Harris_Data/survey_891105.rds")
